低同源蛋白质的二级结构预测至今仍然是一个困难的问题。
Prediction of lowly homological protein secondary structure is still a difficult problem up to now.
这是一个纤维蛋白质的二级结构怎样帮助食品工业的一个例子。
This is an example of HOW the secondary structure of fibrous proteins could help the food industry.
本文中，我们利用数据挖掘得到的统计信息数据库对蛋白质的二级结构进行了预测。
However this process is quite tough. Here our statistical information database by data mining is used for predicting protein secondary structure.
对蛋白质结构数据库的冗余数据进行分析研究表明，目前国际数据库中的蛋白质的二级结构存在误差，误差率为25%左右。
The redundancy in the database of protein structure is analyzed, that the protein secondary structure of the international database error is around 25%.
当六氟异丙醇浓度范围为0.5％-1.5％时，蛋白质的二级结构和内源荧光发射峰位都没有发生明显的变化，而外源ANS荧光强度则显著增强。
In 0.5%-1.5% HFIP solutions, no remarkable changes of secondary structure and fluorescence emission maximum of AK have been observed, while the ANS increased obviously.
文章针对蛋白质二级结构预测这一复杂非线性模式分类问题，提出了基于径向基函数的预测方法。
Aiming at solving the complicated non-linear pattern classification problem of protein secondary structure prediction, a new method based on radial basis function is proposed.
说明：这是一个用于计算蛋白质或短肽二级结构的软件，内附有使用说明！
This is a used to calculate the protein or peptide secondary structure software, included with instructions!
自1940年代以来，已有许多不同构形的蛋白质二级结构陆续被提出。
Since 1940, various conformations have been proposed for the secondary structure of protein.
论文的另一个研究主题是蛋白质二级结构的预测方法。
Another research subject is the prediction of protein secondary structure.
并讨论了蛋白质肽链和溶剂环境对肽段二级结构的调制作用。
Also, the modulation of polypeptide chain and solvent environment on secondary structure of peptide segment was discussed.
为了提高蛋白质二级结构预测精度，本文尝试采用一种基于串联BP网络集成的二级结构预测模型。
To improve the prediction results of protein secondary structure, we developed a neural network ensemble model based on dual-layer feed forward BP network.
从信息论的角度出发，研究了蛋白质序列与二级结构的关联性质。
Starting form information theory, the correlation properties between primary sequence and secondary structure of protein were researched.
近期，有关可能折叠成蛋白质二级结构低聚体的设计、合成工作已备受关注。
In recent years there has been intense interest in the design of pseudo-peptide oligomers that fold into well - defined secondary structures.
提出了根据蛋白质二级结构主序列对蛋白质结构型进行识别(分类)的方法。
Based on this, two methods for the recognition of the structural class of a protein are proposed.
方法：采用远紫外圆二色谱分析法，对正常人及甲旁亢患者血清的PTH(1 -34)进行蛋白质二级结构的测定。
Methods: To determine the secondary structure of PTH (1-34) in normal and hyperparathyroidism condition using far-UV circular dichroism spectroscopy.
在第五章，我们研究了二级结构对蛋白质折叠属性的影响。
In Chapter 5, the protein folding properties under the effect of secondary structural elements are investigated.
蛋白质二级结构预测是蛋白质结构预测的重要组成部分，是蛋白质结构预测最关键的步骤。
Protein secondary structure prediction becomes the most important step of predicting the space conformation from protein molecule.
本文主要论述了神经网络在预测蛋白质二级结构方面的进展以及存在的问题。
Progress and problems in the field of predicting protein secondary structure by neural networks are presented in this paper.
摘要：介绍了构造性机器学习方法——覆盖算法在蛋白质二级结构预测中的应用。
Absrtact: Mainly introduces protein secondary structure prediction based on structural machine learning-covering algorithm.
表明： 基于氨基酸组成和有偏自协方差函数为特征矢量的BP神经网络预测蛋白质二级结构含量的方法可有效提高预测精度。
It is shown that the BP neural network method combined with the amino-acid composition and the biased auto-covariance function features could effectively improve the prediction accuracy.
蛋白质的一种二级结构，相对平坦，在两条平行的或反向平行的肽段之间形成氢键。
A secondary structure in proteins, relatively flat and formed hydrogen bonding between two parallel or anti-parallel stretches of polypeptide.
本文选择蛋白质二级结构数据为主要的研究对象，应用数据挖掘技术和机器学习中的动态规划理论进行蛋白质结构分类。
Protein second structure data is chosen as main study object, and data mining and dynamic programming are applied to protein structure classification.
蛋白质二级结构是研究蛋白质结构和功能的基础和重要组成部分。
Protein secondary structure prediction is a fundamental and important component in the study of protein structure and functions.
蛋白质二级结构是研究蛋白质结构和功能的基础和重要组成部分。
Protein secondary structure prediction is a fundamental and important component in the study of protein structure and functions.
应用推荐